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  "Title": "Fast Calculation of Feature Contributions in Boosting Trees",
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  "Date": "2026-08-22",
  "Authors@R": "c(\nperson(\"Steven\", \"He\", role = \"aut\", email = \"hess1@uci.edu\"),\nperson(\"Zhongli\", \"Jiang\", role = c(\"aut\", \"cre\"),\nemail = \"zhongli.jiang.stats@gmail.com\"),\nperson(\"Dabao\", \"Zhang\", role = \"aut\", email = \"zdb969@hs.uci.edu\"))",
  "Description": "Computes feature-specific R-squared (R2) contributions for\nboosting tree models using a Shapley-value-based decomposition\nof the total R-squared in polynomial time. Supports models\nfitted with 'XGBoost', 'LightGBM', and 'CatBoost', with\noptimized backend-specific implementations and cached tree\nsummaries suitable for large-scale problems. Multiple\nvisualization tools are included for interpreting and\ncommunicating feature contributions. The methodology is\ndescribed in Jiang, Zhang, and Zhang (2025)\n<doi:10.48550/arXiv.2407.03515>. Optional 'CatBoost' support\nuses the R package 'catboost', which is not distributed on\nCRAN; installation instructions and released binaries are\nprovided by the CatBoost project at\n<https://catboost.ai/docs/en/concepts/r-installation>.",
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}